Showing 1 - 11 results of 11 for search '(( primary aim guided optimization algorithm ) OR ( binary mapk driven optimization algorithm ))', query time: 0.51s Refine Results
  1. 1

    Datasets used for the study and their sources. by Peter N-jonaam Mahama (15347793)

    Published 2023
    “…Projecting into 2030, this study aimed at providing geographical information data for guiding future policies on siting required healthcare facilities. …”
  2. 2

    Overview of study assessments of the trial. by Tan Boon Toh (1348143)

    Published 2024
    “…</p><p>Methods</p><p>This is an interventional, non-randomized, open-label study, which aims to enroll 10 patients who will receive QPOP-guided chemotherapy at the time of first HGG recurrence following progression on standard first-line therapy. …”
  3. 3

    Image_4_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

    Published 2023
    “…A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. …”
  4. 4

    Image_5_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

    Published 2023
    “…A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. …”
  5. 5

    Image_3_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

    Published 2023
    “…A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. …”
  6. 6

    Image_1_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

    Published 2023
    “…A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. …”
  7. 7

    Image_2_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

    Published 2023
    “…A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. …”
  8. 8

    DataSheet_1_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal chola... by Di Wang (329735)

    Published 2023
    “…A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. …”
  9. 9

    Flowchart of screening and inclusion. by Jennifer S. Breel (15285263)

    Published 2023
    “…Existing point-of-care coagulation testing guided algorithms for optimizing perioperative coagulation management possibly need to be adjusted for these high-risk patients undergoing cardiac surgery.…”
  10. 10

    DataSheet_1_A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn... by Gaosen Zhang (539619)

    Published 2022
    “…Background<p>This study aimed to determine an optimal machine learning (ML) model for evaluating the preoperative diagnostic value of ultrasound signs of breast cancer lesions for sentinel lymph node (SLN) status.…”
  11. 11

    Supplementary file 1_A study on a real-world data-based VTE risk prediction model for lymphoma patients.docx by Changli He (22424818)

    Published 2025
    “…</p>Results<p>Combining different imputation, sampling, and feature selection strategies yielded 27 datasets, which were trained across nine algorithms to generate 243 models. The optimal model—Simp-SMOTE_rf_GBM, constructed using random forest imputation, SMOTE oversampling, and gradient boosting machine—achieved the highest predictive performance (AUC = 0.954). …”